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           <a class="entry-title" href="http://blog.cos.name/taoshi/2014/05/12/%e6%9c%9d%e8%8a%b1%e5%a4%95%e6%8b%be%ef%bc%9a%e8%bf%8e%e6%8e%a5%e4%bf%a1%e6%81%af%e6%97%b6%e4%bb%a3%e7%9a%84%e7%bb%9f%e8%ae%a1%e6%8c%91%e6%88%98/" rel="bookmark" title="[朝花夕拾] 迎接信息时代的统计挑战">
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           5月
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           12
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           2014
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        <span class="updated" title="2014-05-12T21:33:30+00:00">
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         <div class="entry entry-content fix">
          <p>
           世事变迁，最近居然忽然有时间坐下来读些东西，重新审视一下这几年统计领域的发展了。粗略回想了一下，惭愧地发现我好像连
           <a href="http://www.stat.berkeley.edu/~binyu" target="_blank">
            博士导师
           </a>
           的文章也没有系统地读过几篇，只是和自己研究有关的才详细夜读过。于是把她的一些老文章翻出来认真读读，了解一下她对统计研究和学科发展的观点。
          </p>
          <p>
           木然回首，那文已在灯火阑珊处。2007年时她写过一篇综述性文章：
           <a href="http://www.stat.berkeley.edu/~binyu/ps/embracing.pdf" target="_blank">
            Embracing Statistical Challenges in the Information Technology Age
           </a>
           , Bin Yu (2007)
           <em>
            Technometrics
           </em>
           , 49(3), 237-248。摘要如下：
          </p>
          <blockquote>
           <p>
            This article examines the role of statistics in the age of information technology (IT). It begins by examining the current state of IT and of the cyber infrastructure initiative aimed at integrating the technologies into science, engineering, and education to convert massive amounts of data into useful information. Selected applications from science and text processing are introduced to provide concrete examples of massive data sets and the statistical challenges that they pose. The thriving field of machine learning is reviewed as an example of current achievements driven by computations and IT. Ongoing challenges that we face in the IT revolution are also highlighted. The paper concludes that for the healthy future of our field, computer technologies have to be integrated into statistics, and statistical thinking in turn must be integrated into computer technologies.
           </p>
          </blockquote>
          <p>
           文中指出统计当今面临的最大挑战是“统计思维与计算技术的相互结合”。其中提到的一个主要例子还就是我博士论文的课题之一：设计更好更快的算法来区分NASA的卫星在南北极拍摄到图片中的空中云层和地面冰雪。
          </p>
          <p>
           <img src="http://i.imgur.com/cyYcPQT.jpg"/>
          </p>
          <p>
           这工作前前后后持续了超过六年的时间。现在回头想想，从中学习到的和体会到的还是挺有好玩的，同时也发现当时还是很二的。文中提到的那些点点滴滴可谓是历历在目，处处伤心泪啊！
          </p>
          <p>
           遥想当年，数据好难得。在合作者的帮助下也等了三个月才拿到两幅云图。唯一知道的就是专家相信卫星拍到的图中背景是冰天雪地的北极苦寒之地，其中有些部分有云层覆盖。记得和彬两个人第一次盯着图看时，彬问：“哪里是云？”我答：“我（gui）哪（cai）知道。” 就这样的开始了那么多年研究。
          </p>
          <p>
           <img src="http://i.imgur.com/aIO3H9A.jpg"/>
          </p>
          <p>
           哎！说你呢：“一不小心就歪楼了。” “是，是，这里面的血泪和欢心故事改天另起个楼。。。“
          </p>
          <p>
           先跳过中间过程，来看一下文中的总结。The cloud detection experience reveals several considerations that arise in interdisciplinary research involving large amounts of data:
          </p>
          <div title="Page 3">
           <div title="Page 3">
            <ol>
             <li>
              Access to good scientific or subject problems and expertise
             </li>
             <li>
              Collection and management of large data sets (including effective transmission and storage and possibly data reduction or feature selection)
             </li>
             <li>
              EDA (visualization and descriptive statistics and possibly also data reduction or feature selection)
             </li>
             <li>
              Processing mode: offline or online (streaming data)
             </li>
             <li>
              Formal modeling with computation and accuracy considerations (estimation and uncertainty assessment)
             </li>
             <li>
              Data fusion from various sources
             </li>
             <li>
              Validation using information from outside statistics(quantitative test data or qualitative validation based on subject matter).
             </li>
            </ol>
           </div>
           <p>
            这总结真是贴心啊。把它们和最近风行的大数据的4V特点（Volume（大量）、Velocity（高速）、Variety（多样）、Value（价值））比一下，好像大V们不太关心专家意见和最后的独立验证，但更关心价值。要不说大数据是公司做的，大科学是学术做的呢 😉
           </p>
           <p>
            开个玩笑啦，其实也不能这么字面理解，价值的体现最终还是在从数据中学到的规律能否预测未知，也就是独立验证。
           </p>
           <p>
            <a href="http://www.stat.berkeley.edu/~binyu/ps/embracing.pdf" target="_blank">
             文章
            </a>
            从跨学科统计研究开始，用她的各种研究经历论述计算与机器学习和它们带来的新机遇，还有当时看大数据（massive data，那时big data 这词还没现在如此时尚）对未来统计发展将带来的改变和展望。文章结尾部分对掌握统计技能之外的其他能力的重要性也有提及：
           </p>
           <div title="Page 10">
            <blockquote>
             <p>
              In addition to statistical skills, social and interpersonal skills are needed to successfully collaborate with scientists and persuade them of the key role of statistics in scientific investigations. The importance of these nontechnical skills in interdisciplinary research suggests the need for a culture change in our community and for these nontraditional skills to be valued and recognized in, for example, tenure reviews, promotions, and awards. Last but not least, we need to educate our graduate and undergraduate students with the relevant technical and interpersonal skills.
             </p>
            </blockquote>
            <div title="Page 11">
             <p>
              最后当然是以眺望远方的姿势给大家希望喽：
             </p>
             <blockquote>
              <p>
               This is a time of data deluge; we can help build the ark and ride on it,
               <strong>
                if we so choose.
               </strong>
              </p>
             </blockquote>
             <p>
              不管你读，或者不读，
              <a href="http://www.stat.berkeley.edu/~binyu/ps/embracing.pdf" target="_blank">
               文章都在那里
              </a>
              ；不管你信，或者不信，时间都会揭晓答案。读后马上感觉有希望哦。
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          at 21:33
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           信息时代
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           大数据
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           统计
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          ,
          <a href="http://blog.cos.name/taoshi/tag/%e7%bb%9f%e8%ae%a1%e8%ae%a1%e7%ae%97/" rel="tag">
           统计计算
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           <a class="entry-title" href="http://blog.cos.name/taoshi/2013/07/10/a-conversation-with-professor-bin-yu/" rel="bookmark" title="A conversation with Professor Bin Yu">
            A conversation with Professor Bin Yu
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           7月
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           10
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           2013
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          <p>
           去年就答应了
           <a href="http://www.icsa.org" target="_blank">
            泛华统计协会
           </a>
           去采访博士论文导师
           <a href="http://www.stat.berkeley.edu/~binyu" target="_blank">
            郁彬
           </a>
           ，今年二月时才借道成行，在Berkeley做了采访。
           <a href="http://www.stat.osu.edu/~taoshi/research/papers/Conversation_with_Bin_YU.pdf" target="_blank">
            采访全文
           </a>
           刚刚在
           <a href="http://www.icsa.org/bulletin/index.html" target="_blank">
            这期的 ICSA Bulletin
           </a>
           上登出。
          </p>
          <p>
           先说一句：采访比较长，而且是英文，请有耐心时再读。（注：曾经给某达人试读过关于中国统计看法的部分，刚好赶上人家情绪不好，被直接无视。）
          </p>
          <p>
           我以自己的偏好摘抄几段：
          </p>
          <div title="Page 1">
           <p>
            [Before College]
           </p>
           <blockquote>
            <p>
             A math book from a cousin gave me my first boost into math when I was in 3
             <sup>
              rd
             </sup>
             and 4
             <sup>
              th
             </sup>
             grade. I enjoyed taking exponentials and logarithms using a table in the book.  I  believe doing the math problems provided a refuge of certainty and safety for me during a very turmoil time in China.
            </p>
            <p>
             Another big boost in my interest in mathematics occurred when I was in the Lab School of Normal University in Harbin.  There I had a wonderful and extremely talented sub math teacher, Jianye  Chen (陈建业) in my second year in junior high. [……] Under his strong influence and, in some sense, fulfilling his unrealized dream of going to the math department at Peking University, I chose to do math at Peking University after receiving a very good score on the national college entrance examination in 1980.
            </p>
           </blockquote>
           <p>
            [PKU]
           </p>
           <blockquote>
            <p>
             The first math analysis discussion class was hard for me since I didn’t know how to do the problems. But you know, I really liked math and we had good professors. We didn’t interact a lot with the professors, because that was not the norm.
            </p>
            <p>
             In the entrance exam to graduate school in Peking University, I came first in the math subject exams. However, the professor I wanted to work with did not take me after the oral exam. So I switched into Probability and Statistics, although I originally wanted to do Functional Analysis. That was actually a very good move, a forced one, but it has benefited me tremendously.
            </p>
           </blockquote>
           <p>
            [Qualify Exam at Berkeley]
           </p>
           <blockquote>
            <p>
             <i>
              Shi:
             </i>
             Is it the same format as we took it? 10 questions?
            </p>
            <p>
             <i>
              Yu:
             </i>
             Yes. If you do three, I think, you pass.
            </p>
           </blockquote>
           <p>
            [Marriage]
           </p>
           <blockquote>
            <p>
             In the summer of 1987, I went back to China and got married to my boyfriend who went to graduate school in China in 1985 in architectural history. He was able to join me a year later in Berkeley and went to Berkeley’s School of Architecture. My American friends were a bit shocked to hear that I married someone that I hadn’t seen for two years. It was a bit risky, but looking back, it was the best decision in my life.
            </p>
           </blockquote>
           <p>
            [Suggestion for Young Researchers]
           </p>
           <blockquote>
            <p>
             So I would say to junior people who just started their career: take more risks, instead of being more careful. If you work in a very desirable field like Statistics, you could not go too wrong. Ultimately, whether you enjoy your life or not is because whether you are happy, not because you make the system happy. And the system actually becomes happy because you are happy.
            </p>
           </blockquote>
           <p>
            [Current Status of Statistics]
           </p>
           <blockquote>
            <p>
             I think we are in a golden area for Statistics as an intellectual field. But this field has to be broadly interpreted. Basically a lot of people trained in other fields are also doing this type of work we do.
            </p>
            <p>
             I think if we rise up to the challenge, we will be the leading data scientists. With our great traditions of critical thinking with us, at the same time, embracing machine learning, database, and computing challenges.
            </p>
            <p>
             You take some risks, and you cannot really “fail” too much. You have a safe net. You have a Ph.D. in Statistics. How wrong could it go, right?
            </p>
           </blockquote>
           <p>
            [Statistics in China]
           </p>
           <blockquote>
            <p>
             <i>
              Shi
             </i>
             : By talking with people in China, I do feel industry, especially the high-tech companies, has a huge need for people who can analyze their growing volume of data. Meanwhile, in more scientific area like Biology and Physics, they do have the same need to find people who can work with them in designing and analyzing their experiments and do better science. Is there anything universities in China can do to help foster this type of collaboration?
            </p>
            <p>
             <i>
              Yu
             </i>
             : I think it is kind of happening already. Peking University is talking about a data science center. You have to have cross discipline centers. Any culture change is going to be a slow process. But when there is a need, especially for economic reasons, things just happen in the end. The statistics majors in China, and here too, have to get on top of computing. At senior level, it is easy to find collaborators because you have ideas and a record. If you are a beginner and you cannot even touch the data, who’s going to hire a statistics undergraduate to give advice to a CS undergraduate? It is a constant struggle that we should keep up with computing training of our students. Eventually I hope we will be just as good as computer science majors. That would be the goal, then we will have both the critical thinking and computing skills. I’m not worried about the mathematical part as much not because it is not important. We have been giving our students that, so it is not the urgent need.  The weaker point is the cross-field critical thinking and computing for statistics students.
            </p>
           </blockquote>
           <p>
            [Statistics and Data Science]
           </p>
           <blockquote>
            <p>
             Yu: [……] Lots of people think of statistics as counting numbers, but they don’t know all the exciting things we do. That’s a misconception. Either we go all the way out as a community to change it, which is an uphill battle, or we just embrace data science. Just start saying that we do data science. It is psychology. This is a personal opinion, not representing the view of IMS. I’m just wondering and I think it is a discussion worth having because of the popular unfavorable misconception of statistics.
            </p>
            <p>
             <i>
              Shi
             </i>
             : Yes. I have colleagues who seldom read the Annuals of Statistics. They think the journal mainly concerns about theoretical results and mainly about asymptotic, but they are not.
            </p>
            <p>
             <i>
              Yu
             </i>
             : It is a dilemma in China. Statistics (统计) is 一级学科. Data science is not one of the 学科 yet. But in certain occasions, we can say that we do data science. We are statisticians and we do data science. At least we should go that far.
            </p>
           </blockquote>
           <p>
            [Statistics and Critical Thinking]
           </p>
           <blockquote>
            <p>
             <i>
              Yu
             </i>
             : That’s a gradual process. As I feel being the chair is confronting different opinions. As you said, you cannot form critical thinking without people counter you, even just playing the devil’s advocate. If it is all “great”, it is not critical thinking. Critical thinking is not the most natural thing in the Chinese culture because we tend to want to agree with each other, which has strength in lots of situations, but not in Science. It is something I think the western culture has an edge. In the Chinese culture, there are things called “思辨”and “承传”, but it is more about listening to others than questioning.
            </p>
            <p>
             I’m not disapproving by critiquing, but some students might take that way. So the challenge to me is how to train those students to become critical thinkers. It is almost like they have to establish confidence first somehow.
            </p>
           </blockquote>
           <p>
            [Data Collection and Quality in China]
           </p>
           <blockquote>
            <p>
             <i>
              Shi
             </i>
             : I found it amazing to see on the Internet that comments about any data or any article written by Bureau of Statistics of China are usually like people don’t trust any of them. It seems don’t matter what the report is about. When it says something is good, they don’t trust it; when it says something is bad, they don’t trust it.
            </p>
            <p>
             <i>
              Yu
             </i>
             : Yeah, that’s a big problem you bring up that is data quality. It is not unrelated to plagiarism in doing research at every level. For statistics, if we cannot trust the data, we are done. Maybe theoretical statistics will develop further first before data analysis or data science. But companies care a lot more about good quality of data. They cannot fake their data as much because it is related with their revenue. That’s why I say industry would play a huge role in pushing the development of statistics or data science, whatever it is called, in China.
            </p>
           </blockquote>
           <p>
            我知道，我知道，光摘要就这么长。。。 要是你已经读到这了，那，
           </p>
           <p>
            恭喜你！你的耐心和对统计学科（数据科学）的兴趣足以支持你读
            <a href="http://www.stat.osu.edu/~taoshi/research/papers/Conversation_with_Bin_YU.pdf" target="_blank">
             完全部访谈
            </a>
            的。
           </p>
           <p>
            我保证你会有不同的收获（最少可以看到彬姐在足球队里的风采）。
           </p>
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            Teaching with David Freedman
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         <div class="date">
          <span class="month">
           11月
          </span>
          <span class="day">
           18
          </span>
          <span class="year">
           2011
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          <p>
           In the last semester at Berkeley, I worked as a Graduate Student Instructor (GSI, the name for teaching assistant in the UC system). The course was an undergraduate intro statistics class and the instructor is David Freedman. When I got his email asking me to be his TA, I had no choice except agreeing. In my advisor’s words, “When he asks someone to be his TA, treat it as an honor and just do it.”
          </p>
          <p>
           When David passed away in October 2008 at age 70, the
           <a href="http://berkeley.edu/news/media/releases/2008/10/20_freedman.shtml" target="_blank">
            press release from Berkeley
           </a>
           noted:
          </p>
          <p>
           <em>
            “Freedman’s transition from being a mathematical statistician to a creative practitioner of applied statistics occurred in part, by his own account, in response to the challenges of undergraduate teaching on the UC Berkeley campus,” Collier said. “His students were bored with statistics courses and with the abstracted examples that were standard fare in textbooks,” leading Freedman to dig up practical examples in many applied areas. [……]
           </em>
          </p>
          <p>
           <em>
            He wrote six textbooks, including the highly regarded undergraduate text, “
            <a href="http://www.amazon.com/Statistics-4th-David-Freedman/dp/0393929728/" target="_blank">
             Statistics
            </a>
            ,” with co-authors Robert Pisani and Roger Purves, now in its fourth edition. The book is “a widely used undergraduate textbook, crystal clear, a delight to read and to teach from, broad, deep and meticulously accurate in every detail,” Stark said. “It transformed the way many people taught statistics from a formula-driven, plug-in-the-numbers approach to a focus on
            <strong>
             critical thinking
            </strong>
            .”
            <img src="http://ecx.images-amazon.com/images/I/41FTnmlZVbL._SS500_.jpg"/>
           </em>
          </p>
          <p>
           At first, it seemed strange that he insisted me to attend all his lectures. Being a TA, I have never been required to attend the lecture. Sometimes, you just got to do what you were told to do :). I went to his lectures every time and I had to admit at the end that I learned things I have never thought about. He had constantly drawn straightforward but misled conclusions from data and challenged the students to criticize his arguments. You almost never knew he is telling you what to believe or what not to believe before he turned the face of the card. However, he always gave a convincing concluding remark to summarize the whole discussion. It is fascinating to see critical thinking in full display.
          </p>
          <p>
           I clearly remember two pieces of advises he gave me during that semester. When I asked him for advise on job interviews before I went on to my first one, he told me, “Look, I’m a
           <strong>
            famous
           </strong>
           statistician. However, I know nothing about what you are doing. When you gave the talk, you are the most knowledgeable person on these topics in the room. Just tell your story and be yourself. If they like you, they will hire you. If they don’t, there is nothing you can do and nothing to worry about.” His words gave me confidence to behave just like myself without worrying.
          </p>
          <p>
           He offered me another piece of advisor after we finished grading the finals in his house. As I was walking towards the door, I somehow felt emotional and wanted to say a few words to him. I wanted to thank him for his support and guidance. He looked calmly and told me, “Just do what you feel right to do, and don’t look around.” It was the last time I talked to him. Very often, I feel I owe him so much. I cannot pay anything back to him and the best I can do is passing what I learned from him to others. This is the main reason why I’m writing these blog posts — it just feels right.
          </p>
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            Statistical consulting with David Freedman
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          </span>
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           12
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          <span class="year">
           2011
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          <p>
           One year after taking applied statistics course from David, I signed up for his second “class”,
           <a href="http://www.stat.berkeley.edu/43" target="_blank">
            statistical consulting
           </a>
           . It was a rare opportunity  to learn from someone with so much experience in consulting.
          </p>
          <div class="wp-caption aligncenter" style="width: 410px">
           <img src="http://owpdb.mfo.de/photoNormal?id=9385"/>
           <p class="wp-caption-text">
            David A. Freedman (copyright: George M. Bergman)
           </p>
          </div>
          <p>
           According to
           <a href="http://en.wikipedia.org/wiki/David_A._Freedman_(statistician)" target="_blank">
            Wikipedia
           </a>
           , “Freedman was a consulting or testifying expert on statistics in disputes involving
           <a href="http://en.wikipedia.org/wiki/Employment_discrimination" title="Employment discrimination">
            employment discrimination
           </a>
           , fair loan practices,
           <a href="http://en.wikipedia.org/wiki/Voting_rights" title="Voting rights">
            voting rights
           </a>
           , duplicate signatures on petitions, railroad taxation,
           <a href="http://en.wikipedia.org/w/index.php?title=Ecological_inference&amp;action=edit&amp;redlink=1" title="Ecological inference (page does not exist)">
            ecological inference
           </a>
           , flight patterns of
           <a href="http://en.wikipedia.org/wiki/Golf_ball" title="Golf ball">
            golf balls
           </a>
           ,
           <a href="http://en.wikipedia.org/w/index.php?title=Price_scanner&amp;action=edit&amp;redlink=1" title="Price scanner (page does not exist)">
            price scanner
           </a>
           errors,
           <a href="http://en.wikipedia.org/wiki/Bovine_Spongiform_Encephalopathy" title="Bovine Spongiform Encephalopathy">
            Bovine Spongiform Encephalopathy
           </a>
           (Mad Cow disease), and sampling. He consulted for the
           <a href="http://en.wikipedia.org/wiki/Bank_of_Canada" title="Bank of Canada">
            Bank of Canada
           </a>
           , the
           <a href="http://en.wikipedia.org/w/index.php?title=Carnegie_Commission&amp;action=edit&amp;redlink=1" title="Carnegie Commission (page does not exist)">
            Carnegie Commission
           </a>
           , the
           <a href="http://en.wikipedia.org/wiki/San_Francisco" title="San Francisco">
            City of San Francisco
           </a>
           , the
           <a href="http://en.wikipedia.org/wiki/County_of_Los_Angeles" title="County of Los Angeles">
            County of Los Angeles
           </a>
           , and the
           <a href="http://en.wikipedia.org/wiki/Federal_Reserve" title="Federal Reserve">
            Federal Reserve
           </a>
           , as well as the U.S. departments of energy, treasury, justice, and commerce.”
          </p>
          <p>
           Guess that was the first task we were assigned for the class? Wondering around the campus and posting posters in every building. I know it sounds like old school, but we needed clients :). When there is no problem, there is no need for data or analysis.
          </p>
          <p>
           I remember the first project my partner and I run into was money involved. We were excited to help predicting which alumni has a high tendency to donate back to Berkeley. It was funny that zip code was such a powerful predictor and it is almost universally powerful in most of social studies (as David told us). “location, location, and location!”
          </p>
          <p>
           The most practical (and painful) lesson we learned from David in consulting is that he insisted us not to give any suggestion in the first meeting. See, “we are eager to help, but … ” We should not give answers or suggestions to any problem without understanding its background, even if what the client asked is just how to calculate a p-value using a t-test. It is funny how our profession has squeezed into this corner. In many situations, as soon as one gets a p-value &lt; 0.05, one hypothesis is rejected and (statistically) significant finding is claimed. Ironically, statisticians who created the p-values are the ones who are acting like policemen holding the last stands now. Critically investigating the problem and the data together may push us a few steps closer to “be useful”. Otherwise, we probably act no better than a software package (like SAS or R).
          </p>
          <p>
           In the meanwhile, we also learned to be creative when working with clients who have already collected their data according a “flawed” design. We need help them. In David’s words, “Treat them as your patients. If your patient is bleeding, it does not help to blame them on how they get into an accident. Stop the bleeding and save him/her first, then educate.”. In many cases, how to help them with an imperfect design or data involves developing and researching for new statistical methods. It all goes back to why statistics exists as a field in the first place: help people answer questions with data.
          </p>
          <p>
           [To be continued ……]
          </p>
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            Learning applied statistics from David Freedman
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          <p>
           <a href="http://blog.cos.name/taoshi/files/2011/11/book.jpg">
            <img src="http://blog.cos.name/taoshi/files/2011/11/book.jpg?w=175"/>
           </a>
           Just suit up for the first time since the job interview season of Winter 2005. As one of those who got promoted this year, I attended the
           <a href="http://library.osu.edu/blogs/librarynews/2011/10/27/faculty-recognition-program-november-7/" target="_blank">
            Faculty Recognition Program
           </a>
           today and got a chance to look closely at the book, “
           <a href="http://www.amazon.com/Statistical-Models-Practice-David-Freedman/dp/0521743850" target="_blank">
            Statistical Models, Theory and Practice
           </a>
           ” by
           <a href="http://www.stat.berkeley.edu/~freedman/" target="_blank">
            David A. Freedman
           </a>
           , that I “donated” (the university is paying for it) to the library. I’m honored to be able to pay my tribute to him, who open my world of useful statistics.
          </p>
          <p>
           The firs time I met David was when I took his applied statistic class at Berkeley. At that time, I passed my Ph.D. qualify exam, started with some research projects and felt like I knew Statistics. In his class, I learned quickly that I knew NOTHING about Statistics except some abstract mathematical symbols.
          </p>
          <p>
           The first meeting of the class was the most wield period I have sit thought as a student. Several days before the first class, every student got his email in which a list of three papers were assigned for reading. We were good students so we all read the paper carefully, I assume at least. Then here came the first meeting of the class. David came in and explained several things about the course structures before we went to the more serious business, paper discussion.
          </p>
          <p>
           The discussion started like this. He calmly sit in a chair and asked “questions?”. Silence, …, silence, …, and longer silence. We had not expected that we were supposed to “initiate” the discussion instead of “participating”. Then he asked gain, “questions on the paper?”. We just sit there for more than 10 minutes, flipping through papers  and trying to find a meaning question to ask. Seems like we would sit there forever if we did not come up with any questions. It felt like the longest class I have ever taken.
          </p>
          <p>
           The format of the class kept the same way for the whole semester. Students first read the assigned papers and come up with questions about anything and everything about the paper. David entertained every question. The questions ranges from “why have the authors picked this model?” to “why have the authors NOT picked that model?”, from “Does the model fit the data well?” Do the data support the model at all?”, from “Does the evidence support the conclusion?” to “Is the evidence related to the conclusion?”. The interesting part is that about one third of the papers we discussed were written by him. We have to (at least try to) criticize the paper and he enjoys doing do as well. Sometime he would defend the paper for while and then add his own critics.
          </p>
          <p>
           Trying to recall what I have learned from the class, the most striking thing is that I cannot remember what models or methods we went over (there are a lot of them). I have to admit that I have a really bad memory. At the same time, I do remember one thing: Check the model assumptions and check them again and again.
          </p>
          <p>
           In an age of having so much computation power and advanced software packages to run almost any analysis in a short period time, our ability in drawing proper conclusion from data through mathematical inference is not necessarily improved unless we carefully check if we are using the right tools to answer the right questions. Thinking critically about each step of any analysis is the key to a proper conclusion. This is the first lesson I learned from David.
          </p>
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           [To be continued ……]
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           co-author: 陈甜
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          <p>
           学统计和做统计的大概都认为统计也算是一门学问（“大概”对统计很重要）。很多人在学习的过程中或早或晚都会听到过这样的说法：“统计中重要的不只是我们看到的（数据），更重要的是我们没有看到的（总体，抽样偏差，模型偏差，数据处理错误, 。。。）。”对于留学出国申请，很多同学有的一个迷思就是，我知道要有GPA，GRE,和TOFEL成绩，我也知道 Personal Statement 和推荐信很重要，可是录取到底是一个什么样的过程，而教授们重视的又是哪些呢？下面就让我们来分析一下，想一下：
          </p>
          <p>
           大丈夫有所为有所不为。首先最重要的问题就是为什么要出国留学读博士。既然要把这么多的时间和精力投入一件事情，总得给自己一个理由吧。很多人在 Personal Statement 里这么写，“我对统计研究感兴趣”，“我想到国外感受一下”，“别人读了，我为神马不读”，“我妈妈让我读”或者是“我要追的人出国了”等等。可是如果仔细推敲一下，你是不是能用这些说服你自己呢？我们这里谈的是你接下来5,6年的时光甚至是你一辈子的职业走向，而不是中午吃红烧肉还是番茄炒蛋的问题。就说“我想到国外感受一下”这个理由吧，你想要感受可以出国旅游，想读书可以申请短时间的交换，就为了感受一下花5,6年的时间，你自己是不是信服呢？在申请开始之前，请放下手里的事情，好好审视一下自己的内心，给自己一个想要出国读博的理由。因为博士学习是一个很长很辛苦的过程。你会远离自己的亲人和朋友，要自己处理很多状况，而在你为了学习和生活两头忙的时候，很多你的朋友可能已经开始赚钱养家，结婚生子，走上和你全然不同的人生道路。所以最好你能找到一个足以支持你走到最后毕业和更远的读博理由，而不是为了入学邀请和奖学金。理由不足以说服自己的，可以放松一下去看小说了。
          </p>
          <p>
           好。现在你决定要申请了。那么我们就先翻墙去看看围城的另外一面（你可能不知道的），然后再回来看我们应该怎么做。
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